def get_mlm_logits(model, albert_config, mlm_positions):
"""From run_pretraining.py."""
input_tensor = gather_indexes(model.get_sequence_output(), mlm_positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=albert_config.embedding_size,
activation=modeling.get_activation(albert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
)
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[albert_config.vocab_size],
initializer=tf.zeros_initializer(),
)
logits = tf.matmul(input_tensor,
model.get_embedding_table(),
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
return logits
def get_masked_lm_output(
albert_config,
input_tensor,
output_weights,
positions,
label_ids,
label_weights,
):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope('cls/predictions'):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope('transform'):
input_tensor = tf.layers.dense(
input_tensor,
units = albert_config.embedding_size,
activation = modeling.get_activation(albert_config.hidden_act),
kernel_initializer = modeling.create_initializer(
albert_config.initializer_range
),
)
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
'output_bias',
shape = [albert_config.vocab_size],
initializer = tf.zeros_initializer(),
)
logits = tf.matmul(input_tensor, output_weights, transpose_b = True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis = -1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth = albert_config.vocab_size, dtype = tf.float32
)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(
log_probs * one_hot_labels, axis = [-1]
)
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def __init__(self, bert_config, tokenizer):
_graph = tf.Graph()
with _graph.as_default():
self.X = tf.placeholder(tf.int32, [None, None])
self.top_p = tf.placeholder(tf.float32, None)
self.top_k = tf.placeholder(tf.int32, None)
self.k = tf.placeholder(tf.int32, None)
self.temperature = tf.placeholder(tf.float32, None)
self.indices = tf.placeholder(tf.int32, [None, None])
self.MASK = tf.placeholder(tf.int32, [None, None])
self._tokenizer = tokenizer
self.model = modeling.AlbertModel(
config=bert_config,
is_training=False,
input_ids=self.X,
input_mask=self.MASK,
use_one_hot_embeddings=False,
)
self.logits = self.model.get_pooled_output()
input_tensor = self.model.get_sequence_output()
output_weights = self.model.get_embedding_table()
with tf.variable_scope('cls/predictions'):
with tf.variable_scope('transform'):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.embedding_size,
activation=modeling.get_activation(
bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range),
)
input_tensor = modeling.layer_norm(input_tensor)
output_bias = tf.get_variable(
'output_bias',
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer(),
)
logits = tf.matmul(input_tensor,
output_weights,
transpose_b=True)
self._logits = tf.nn.bias_add(logits, output_bias)
self._log_softmax = tf.nn.log_softmax(self._logits, axis=-1)
logits = tf.gather_nd(self._logits, self.indices)
logits = logits / self.temperature
def necleus():
return top_p_logits(logits, self.top_p)
def select_k():
return top_k_logits(logits, self.top_k)
logits = tf.cond(self.top_p > 0, necleus, select_k)
self.samples = tf.multinomial(logits,
num_samples=self.k,
output_dtype=tf.int32)
self._sess = tf.InteractiveSession()
self._sess.run(tf.global_variables_initializer())
var_lists = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='bert')
cls = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='cls')
self._saver = tf.train.Saver(var_list=var_lists + cls)
attns = _extract_attention_weights(bert_config.num_hidden_layers,
tf.get_default_graph())
self.attns = attns
